Overview

Dataset statistics

Number of variables7
Number of observations845552
Missing cells1297165
Missing cells (%)21.9%
Duplicate rows185
Duplicate rows (%)< 0.1%
Total size in memory45.2 MiB
Average record size in memory56.0 B

Variable types

Categorical1
Text5
Numeric1

Alerts

Dataset has 185 (< 0.1%) duplicate rowsDuplicates
CATEGORY_1 is highly imbalanced (77.8%)Imbalance
CATEGORY_3 has 60566 (7.2%) missing valuesMissing
CATEGORY_4 has 778093 (92.0%) missing valuesMissing
MANUFACTURER has 226474 (26.8%) missing valuesMissing
BRAND has 226472 (26.8%) missing valuesMissing

Reproduction

Analysis started2025-03-09 21:30:45.493950
Analysis finished2025-03-09 21:31:01.870927
Duration16.38 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

CATEGORY_1
Categorical

IMBALANCE 

Distinct27
Distinct (%)< 0.1%
Missing111
Missing (%)< 0.1%
Memory size6.5 MiB
Health & Wellness
512695 
Snacks
324817 
Beverages
 
3990
Pantry
 
871
Apparel & Accessories
 
846
Other values (22)
 
2222

Length

Max length22
Median length17
Mean length12.707907
Min length5

Characters and Unicode

Total characters10743786
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHealth & Wellness
2nd rowSnacks
3rd rowHealth & Wellness
4th rowHealth & Wellness
5th rowHealth & Wellness

Common Values

ValueCountFrequency (%)
Health & Wellness 512695
60.6%
Snacks 324817
38.4%
Beverages 3990
 
0.5%
Pantry 871
 
0.1%
Apparel & Accessories 846
 
0.1%
Dairy 602
 
0.1%
Needs Review 547
 
0.1%
Alcohol 503
 
0.1%
Home & Garden 115
 
< 0.1%
Restaurant 69
 
< 0.1%
Other values (17) 386
 
< 0.1%
(Missing) 111
 
< 0.1%

Length

2025-03-09T17:31:01.999924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
513877
27.4%
health 512695
27.4%
wellness 512695
27.4%
snacks 324817
17.3%
beverages 3990
 
0.2%
pantry 871
 
< 0.1%
apparel 846
 
< 0.1%
accessories 846
 
< 0.1%
dairy 602
 
< 0.1%
review 547
 
< 0.1%
Other values (33) 2043
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 1555587
14.5%
l 1540142
14.3%
s 1357553
12.6%
1028388
9.6%
a 844307
7.9%
n 838718
7.8%
& 513877
 
4.8%
t 513857
 
4.8%
h 513270
 
4.8%
H 512834
 
4.8%
Other values (32) 1525253
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10743786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1555587
14.5%
l 1540142
14.3%
s 1357553
12.6%
1028388
9.6%
a 844307
7.9%
n 838718
7.8%
& 513877
 
4.8%
t 513857
 
4.8%
h 513270
 
4.8%
H 512834
 
4.8%
Other values (32) 1525253
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10743786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1555587
14.5%
l 1540142
14.3%
s 1357553
12.6%
1028388
9.6%
a 844307
7.9%
n 838718
7.8%
& 513877
 
4.8%
t 513857
 
4.8%
h 513270
 
4.8%
H 512834
 
4.8%
Other values (32) 1525253
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10743786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1555587
14.5%
l 1540142
14.3%
s 1357553
12.6%
1028388
9.6%
a 844307
7.9%
n 838718
7.8%
& 513877
 
4.8%
t 513857
 
4.8%
h 513270
 
4.8%
H 512834
 
4.8%
Other values (32) 1525253
14.2%
Distinct121
Distinct (%)< 0.1%
Missing1424
Missing (%)0.2%
Memory size6.5 MiB
2025-03-09T17:31:02.320545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length46
Median length35
Mean length12.075909
Min length3

Characters and Unicode

Total characters10193613
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowSexual Health
2nd rowPuffed Snacks
3rd rowHair Care
4th rowOral Care
5th rowMedicines & Treatments
ValueCountFrequency (%)
301082
15.9%
care 246440
 
13.0%
hair 125081
 
6.6%
candy 121036
 
6.4%
treatments 117718
 
6.2%
medicines 99118
 
5.2%
bath 81469
 
4.3%
body 81469
 
4.3%
skin 62587
 
3.3%
nuts 33522
 
1.8%
Other values (182) 625771
33.0%
2025-03-09T17:31:02.874076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1110136
 
10.9%
1051165
 
10.3%
a 976905
 
9.6%
i 676575
 
6.6%
r 671853
 
6.6%
n 573186
 
5.6%
s 534621
 
5.2%
t 522516
 
5.1%
C 449030
 
4.4%
d 418113
 
4.1%
Other values (40) 3209513
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10193613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1110136
 
10.9%
1051165
 
10.3%
a 976905
 
9.6%
i 676575
 
6.6%
r 671853
 
6.6%
n 573186
 
5.6%
s 534621
 
5.2%
t 522516
 
5.1%
C 449030
 
4.4%
d 418113
 
4.1%
Other values (40) 3209513
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10193613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1110136
 
10.9%
1051165
 
10.3%
a 976905
 
9.6%
i 676575
 
6.6%
r 671853
 
6.6%
n 573186
 
5.6%
s 534621
 
5.2%
t 522516
 
5.1%
C 449030
 
4.4%
d 418113
 
4.1%
Other values (40) 3209513
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10193613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1110136
 
10.9%
1051165
 
10.3%
a 976905
 
9.6%
i 676575
 
6.6%
r 671853
 
6.6%
n 573186
 
5.6%
s 534621
 
5.2%
t 522516
 
5.1%
C 449030
 
4.4%
d 418113
 
4.1%
Other values (40) 3209513
31.5%

CATEGORY_3
Text

MISSING 

Distinct344
Distinct (%)< 0.1%
Missing60566
Missing (%)7.2%
Memory size6.5 MiB
2025-03-09T17:31:03.357808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length41
Median length33
Mean length17.102599
Min length3

Characters and Unicode

Total characters13425301
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)< 0.1%

Sample

1st rowConductivity Gels & Lotions
2nd rowCheese Curls & Puffs
3rd rowHair Care Accessories
4th rowToothpaste
5th rowEssential Oils
ValueCountFrequency (%)
260403
 
12.5%
candy 107888
 
5.2%
hair 71523
 
3.4%
treatments 57832
 
2.8%
confection 56965
 
2.7%
supplements 55700
 
2.7%
herbal 55700
 
2.7%
vitamins 55700
 
2.7%
chocolate 47710
 
2.3%
care 44360
 
2.1%
Other values (479) 1265194
60.9%
2025-03-09T17:31:04.040792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1293989
 
9.6%
e 1150071
 
8.6%
a 1047002
 
7.8%
n 907269
 
6.8%
s 876002
 
6.5%
i 866388
 
6.5%
o 790373
 
5.9%
t 773408
 
5.8%
r 700702
 
5.2%
l 446232
 
3.3%
Other values (44) 4573865
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13425301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1293989
 
9.6%
e 1150071
 
8.6%
a 1047002
 
7.8%
n 907269
 
6.8%
s 876002
 
6.5%
i 866388
 
6.5%
o 790373
 
5.9%
t 773408
 
5.8%
r 700702
 
5.2%
l 446232
 
3.3%
Other values (44) 4573865
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13425301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1293989
 
9.6%
e 1150071
 
8.6%
a 1047002
 
7.8%
n 907269
 
6.8%
s 876002
 
6.5%
i 866388
 
6.5%
o 790373
 
5.9%
t 773408
 
5.8%
r 700702
 
5.2%
l 446232
 
3.3%
Other values (44) 4573865
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13425301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1293989
 
9.6%
e 1150071
 
8.6%
a 1047002
 
7.8%
n 907269
 
6.8%
s 876002
 
6.5%
i 866388
 
6.5%
o 790373
 
5.9%
t 773408
 
5.8%
r 700702
 
5.2%
l 446232
 
3.3%
Other values (44) 4573865
34.1%

CATEGORY_4
Text

MISSING 

Distinct127
Distinct (%)0.2%
Missing778093
Missing (%)92.0%
Memory size6.5 MiB
2025-03-09T17:31:04.378217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length47
Median length36
Mean length20.771402
Min length4

Characters and Unicode

Total characters1401218
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowHair Brushes & Combs
2nd rowWomen's Shaving Gel & Cream
3rd rowLip Balms
4th rowAlready Popped Popcorn
5th rowWomen's Shaving Gel & Cream
ValueCountFrequency (%)
31106
 
14.1%
treatments 14079
 
6.4%
medicines 12755
 
5.8%
lip 11063
 
5.0%
popcorn 10719
 
4.9%
balms 9737
 
4.4%
hair 7892
 
3.6%
already 6974
 
3.2%
popped 6974
 
3.2%
women's 6170
 
2.8%
Other values (200) 103029
46.7%
2025-03-09T17:31:04.897672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153039
 
10.9%
e 146700
 
10.5%
s 96052
 
6.9%
i 84418
 
6.0%
n 84302
 
6.0%
a 82621
 
5.9%
r 82474
 
5.9%
o 75600
 
5.4%
t 56913
 
4.1%
p 50709
 
3.6%
Other values (43) 488390
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1401218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
153039
 
10.9%
e 146700
 
10.5%
s 96052
 
6.9%
i 84418
 
6.0%
n 84302
 
6.0%
a 82621
 
5.9%
r 82474
 
5.9%
o 75600
 
5.4%
t 56913
 
4.1%
p 50709
 
3.6%
Other values (43) 488390
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1401218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
153039
 
10.9%
e 146700
 
10.5%
s 96052
 
6.9%
i 84418
 
6.0%
n 84302
 
6.0%
a 82621
 
5.9%
r 82474
 
5.9%
o 75600
 
5.4%
t 56913
 
4.1%
p 50709
 
3.6%
Other values (43) 488390
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1401218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
153039
 
10.9%
e 146700
 
10.5%
s 96052
 
6.9%
i 84418
 
6.0%
n 84302
 
6.0%
a 82621
 
5.9%
r 82474
 
5.9%
o 75600
 
5.4%
t 56913
 
4.1%
p 50709
 
3.6%
Other values (43) 488390
34.9%

MANUFACTURER
Text

MISSING 

Distinct4354
Distinct (%)0.7%
Missing226474
Missing (%)26.8%
Memory size6.5 MiB
2025-03-09T17:31:05.355914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length54
Median length41
Mean length17.130166
Min length2

Characters and Unicode

Total characters10604909
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique553 ?
Unique (%)0.1%

Sample

1st rowPLACEHOLDER MANUFACTURER
2nd rowCOLGATE-PALMOLIVE
3rd rowMAPLE HOLISTICS AND HONEYDEW PRODUCTS INTERCHANGEABLY.
4th rowPLACEHOLDER MANUFACTURER
5th rowHALEON
ValueCountFrequency (%)
manufacturer 108900
 
7.3%
inc 87087
 
5.8%
placeholder 86902
 
5.8%
49150
 
3.3%
llc 48309
 
3.2%
company 47379
 
3.2%
the 27173
 
1.8%
johnson 23485
 
1.6%
foods 22006
 
1.5%
procter 21065
 
1.4%
Other values (5168) 974130
65.1%
2025-03-09T17:31:06.051899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1032996
 
9.7%
A 904316
 
8.5%
876508
 
8.3%
R 869407
 
8.2%
L 706251
 
6.7%
O 684769
 
6.5%
N 680588
 
6.4%
C 672156
 
6.3%
T 482682
 
4.6%
I 468787
 
4.4%
Other values (43) 3226449
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10604909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1032996
 
9.7%
A 904316
 
8.5%
876508
 
8.3%
R 869407
 
8.2%
L 706251
 
6.7%
O 684769
 
6.5%
N 680588
 
6.4%
C 672156
 
6.3%
T 482682
 
4.6%
I 468787
 
4.4%
Other values (43) 3226449
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10604909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1032996
 
9.7%
A 904316
 
8.5%
876508
 
8.3%
R 869407
 
8.2%
L 706251
 
6.7%
O 684769
 
6.5%
N 680588
 
6.4%
C 672156
 
6.3%
T 482682
 
4.6%
I 468787
 
4.4%
Other values (43) 3226449
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10604909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1032996
 
9.7%
A 904316
 
8.5%
876508
 
8.3%
R 869407
 
8.2%
L 706251
 
6.7%
O 684769
 
6.5%
N 680588
 
6.4%
C 672156
 
6.3%
T 482682
 
4.6%
I 468787
 
4.4%
Other values (43) 3226449
30.4%

BRAND
Text

MISSING 

Distinct8122
Distinct (%)1.3%
Missing226472
Missing (%)26.8%
Memory size6.5 MiB
2025-03-09T17:31:06.513903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length42
Median length36
Mean length9.9704287
Min length2

Characters and Unicode

Total characters6172493
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1112 ?
Unique (%)0.2%

Sample

1st rowELECSOP
2nd rowCOLGATE
3rd rowMAPLE HOLISTICS
4th rowBEAUHAIR
5th rowEMERGEN-C
ValueCountFrequency (%)
brand 44190
 
4.3%
rem 20813
 
2.0%
not 17366
 
1.7%
known 17025
 
1.7%
private 13743
 
1.3%
label 13468
 
1.3%
8551
 
0.8%
hair 7961
 
0.8%
care 7378
 
0.7%
cvs 6400
 
0.6%
Other values (8213) 864054
84.6%
2025-03-09T17:31:07.207881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 663544
 
10.8%
A 543564
 
8.8%
R 496839
 
8.0%
401869
 
6.5%
S 395096
 
6.4%
N 391809
 
6.3%
O 381562
 
6.2%
I 351955
 
5.7%
T 330375
 
5.4%
L 315642
 
5.1%
Other values (55) 1900238
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6172493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 663544
 
10.8%
A 543564
 
8.8%
R 496839
 
8.0%
401869
 
6.5%
S 395096
 
6.4%
N 391809
 
6.3%
O 381562
 
6.2%
I 351955
 
5.7%
T 330375
 
5.4%
L 315642
 
5.1%
Other values (55) 1900238
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6172493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 663544
 
10.8%
A 543564
 
8.8%
R 496839
 
8.0%
401869
 
6.5%
S 395096
 
6.4%
N 391809
 
6.3%
O 381562
 
6.2%
I 351955
 
5.7%
T 330375
 
5.4%
L 315642
 
5.1%
Other values (55) 1900238
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6172493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 663544
 
10.8%
A 543564
 
8.8%
R 496839
 
8.0%
401869
 
6.5%
S 395096
 
6.4%
N 391809
 
6.3%
O 381562
 
6.2%
I 351955
 
5.7%
T 330375
 
5.4%
L 315642
 
5.1%
Other values (55) 1900238
30.8%

BARCODE
Real number (ℝ)

Distinct841342
Distinct (%)> 99.9%
Missing4025
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.0161091 × 1011
Minimum185
Maximum6.2911082 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 MiB
2025-03-09T17:31:07.475880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum185
5-th percentile1.7082884 × 1010
Q17.124923 × 1010
median6.344185 × 1011
Q37.683955 × 1011
95-th percentile8.91164 × 1011
Maximum6.2911082 × 1013
Range6.2911082 × 1013
Interquartile range (IQR)6.9714627 × 1011

Descriptive statistics

Standard deviation1.0225297 × 1012
Coefficient of variation (CV)1.6996529
Kurtosis81.861678
Mean6.0161091 × 1011
Median Absolute Deviation (MAD)2.5520957 × 1011
Skewness6.1811281
Sum5.0627182 × 1017
Variance1.045567 × 1024
MonotonicityNot monotonic
2025-03-09T17:31:07.694860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11461821 2
 
< 0.1%
20146900 2
 
< 0.1%
3454206 2
 
< 0.1%
3462003 2
 
< 0.1%
3422007 2
 
< 0.1%
906425 2
 
< 0.1%
3451304 2
 
< 0.1%
50426171 2
 
< 0.1%
3423905 2
 
< 0.1%
3416105 2
 
< 0.1%
Other values (841332) 841507
99.5%
(Missing) 4025
 
0.5%
ValueCountFrequency (%)
185 1
< 0.1%
3582 1
< 0.1%
4091 1
< 0.1%
5579 1
< 0.1%
5777 1
< 0.1%
5784 1
< 0.1%
6163 1
< 0.1%
6910 1
< 0.1%
9034 1
< 0.1%
10498 1
< 0.1%
ValueCountFrequency (%)
6.291108161 × 10131
< 0.1%
6.291100733 × 10131
< 0.1%
5.4114457 × 10131
< 0.1%
5.010724528 × 10131
< 0.1%
1.0895178 × 10131
< 0.1%
1.0857602 × 10131
< 0.1%
1.085748401 × 10131
< 0.1%
1.085748401 × 10131
< 0.1%
1.085748401 × 10131
< 0.1%
1.077098104 × 10131
< 0.1%

Interactions

2025-03-09T17:30:58.218013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-09T17:31:07.830857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
BARCODECATEGORY_1
BARCODE1.0000.021
CATEGORY_10.0211.000

Missing values

2025-03-09T17:30:58.600003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T17:30:59.311987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-09T17:31:01.039956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CATEGORY_1CATEGORY_2CATEGORY_3CATEGORY_4MANUFACTURERBRANDBARCODE
0Health & WellnessSexual HealthConductivity Gels & LotionsNaNNaNNaN7.964944e+11
1SnacksPuffed SnacksCheese Curls & PuffsNaNNaNNaN2.327801e+10
2Health & WellnessHair CareHair Care AccessoriesNaNPLACEHOLDER MANUFACTURERELECSOP4.618178e+11
3Health & WellnessOral CareToothpasteNaNCOLGATE-PALMOLIVECOLGATE3.500047e+10
4Health & WellnessMedicines & TreatmentsEssential OilsNaNMAPLE HOLISTICS AND HONEYDEW PRODUCTS INTERCHANGEABLY.MAPLE HOLISTICS8.068109e+11
5Health & WellnessHair CareHair Care AccessoriesNaNPLACEHOLDER MANUFACTURERBEAUHAIR6.626585e+11
6Health & WellnessMedicines & TreatmentsVitamins & Herbal SupplementsNaNHALEONEMERGEN-C6.177376e+11
7Health & WellnessDeodorant & AntiperspirantMen's Deodorant & AntiperspirantNaNNaNNaN7.501839e+12
8SnacksSnack BarsGranola BarsNaNHYVEE INCHY-VEE7.545013e+10
9Health & WellnessNaNNaNNaNCHURCH & DWIGHTREPHRESHNaN
CATEGORY_1CATEGORY_2CATEGORY_3CATEGORY_4MANUFACTURERBRANDBARCODE
845542Health & WellnessHair CareHair ColorNaNL'OREALREDKEN8.844864e+11
845543Health & WellnessSkin CareFacial CleansersNaNNORVELL SKIN SOLUTIONS, LLCNORVELL8.125120e+11
845544SnacksPudding & GelatinReady-to-Eat PuddingNaNPLACEHOLDER MANUFACTURERBRAND NOT KNOWN4.427603e+10
845545SnacksNuts & SeedsCovered NutsNaNNaNNaN7.729091e+10
845546Health & WellnessBath & BodyLiquid Hand SoapNaNNaNNaN7.545029e+10
845547Health & WellnessTopical Muscle & Joint Relief TreatmentsBraces & WrapsNaNNaNNaN7.223016e+11
845548SnacksCookiesNaNNaNTREEHOUSE FOODS, INC.LOFTHOUSE4.182082e+10
845549SnacksCandyConfection CandyNaNHARIBO GMBH & CO KGHARIBO1.001672e+11
845550SnacksNuts & SeedsHazelnutsNaNDOUBLE-COLA COJUMBO7.539076e+10
845551Health & WellnessFirst AidFirst Aid KitsNaN3MNEXCARE7.967933e+11

Duplicate rows

Most frequently occurring

CATEGORY_1CATEGORY_2CATEGORY_3CATEGORY_4MANUFACTURERBRANDBARCODE# duplicates
13RestaurantBeveragesSodaNaNTHE COCA-COLA COMPANYCOCA-COLANaN19
12RestaurantBeveragesSodaNaNPEPSICOPEPSINaN5
10RestaurantBeveragesSlushies & IceesNaNTHE COCA-COLA COMPANYCOCA-COLANaN4
11RestaurantBeveragesSodaDiet SodaPEPSICOPEPSINaN4
2Health & WellnessMedicines & TreatmentsAllergy & Sinus Medicines & TreatmentsNaNHALEONFLONASENaN3
3Health & WellnessMedicines & TreatmentsVitamins & Herbal SupplementsNaNHALEONEMERGEN-CNaN3
152SnacksChipsCrispsNaNKELLANOVAPRINGLESNaN3
174SnacksPuffed SnacksCheese Curls & PuffsNaNTHE HERSHEY COMPANYPIRATE'S BOOTYNaN3
175SnacksPuffed SnacksPopcornNaNTHE HERSHEY COMPANYSKINNYPOPNaN3
176SnacksSnack CakesBrownie Snack CakesNaNBIMBOENTENMANN'S SWEET BAKED GOODSNaN3